mlsanityData to 14 Oct 2026

Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks

arXiv 2306.13690 ↗First posted 22 Jun 2023cs.LG eess.SPcited by 8

Not on the map. Why?

Abstract (arXiv)

As we deal with the effects of climate change and the increase of global atmospheric temperatures, the accurate tracking and prediction of ice layers within polar ice sheets grows in importance. Studying these ice layers reveals climate trends, how snowfall has changed over time, and the trajectory of future climate and precipitation. In this paper, we propose a machine learning model that uses adaptive, recurrent graph convolutional networks to, when given the amount of snow accumulation in recent years gathered through airborne radar data, predict historic snow accumulation by way of the thickness of deep ice layers. We found that our model performs better and with greater consistency than our previous model as well as equivalent non-temporal, non-geometric, and non-adaptive models.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

13 of 24 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2019Searching for MobileNetV3cited by 1,994
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
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  7. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  8. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
  9. 2018Graph R-CNN for Scene Graph Generationcited by 298
  10. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 275
  11. 2020Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networkscited by 15
  12. 2021Multistream Graph Attention Networks for Wind Speed Forecastingcited by 12
1 more
  1. 2023Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radarcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 3.

8 papers.

  1. 2024Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Networkcited by 5
  2. 2024Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Predictioncited by 5
  3. 2025GRIT: Graph Transformer For Internal Ice Layer Thickness Predictioncited by 4
  4. 2026K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Datacited by 2
  5. 2025GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Predictioncited by 1
  6. 2026PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulationcited by 1
  7. 2024Physics-Informed Machine Learning On Polar Ice: A Surveycited by 0
  8. 2026Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Tracescited by 0